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Brain Computer Interface (BCI) Applications: Privacy Threats and Countermeasures
... Several solutions, summarized in Table 1, have been proposed to improve the security of a neural interface [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30]. The proposed methods typically use one or more solutions to supervise the BCI session [17,18], authenticate the user [19][20][21][22][23], encrypt the data [24][25][26][27], and ultimately detect cyberattacks [28][29][30]. ...
... Several solutions, summarized in Table 1, have been proposed to improve the security of a neural interface [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30]. The proposed methods typically use one or more solutions to supervise the BCI session [17,18], authenticate the user [19][20][21][22][23], encrypt the data [24][25][26][27], and ultimately detect cyberattacks [28][29][30]. ...
... P300-driven wheelchair [23] Authentication User-specific action profile User framework [20] User-specific EEG data EEG data [21] P300 BCI [22] Encryption techniques protect the communication between the sensors and the BCI framework, arguably the weakest point in BCI systems. Encryption methods for BCI applications include the use of an anonymizer [29], unconventional tensor-based data representation [30], standard encryption algorithms [18], and randomization [19]. Cyberattack identification software has also been developed to identify threats in a timely manner. ...
In a progressively interconnected world where the internet of things (IoT), ubiquitous computing, and artificial intelligence are leading to groundbreaking technology, cybersecurity remains an underdeveloped aspect. This is particularly alarming for brain-to-computer interfaces (BCIs), where hackers can threaten the user’s physical and psychological safety. In fact, standard algorithms currently employed in BCI systems are inadequate to deal with cyberattacks. In this paper, we propose a solution to improve the cybersecurity of BCI systems. As a case study, we focus on P300-based BCI systems using support vector machine (SVM) algorithms and EEG data. First, we verified that SVM algorithms are incapable of identifying hacking by simulating a set of cyberattacks using fake P300 signals and noise-based attacks. This was achieved by comparing the performance of several models when validated using real and hacked P300 datasets. Then, we implemented our solution to improve the cybersecurity of the system. The proposed solution is based on an EEG channel mixing approach to identify anomalies in the transmission channel due to hacking. Our study demonstrates that the proposed architecture can successfully identify 99.996% of simulated cyberattacks, implementing a dedicated counteraction that preserves most of BCI functions.
... State-Of-The-Art, Opportunities, and Future Challenges 0:3 Existing works of the literature have detected specific security attacks affecting BCI integrity, confidentiality, availability, and safety, but they do not perform a comprehensive analysis and miss relevant concerns [17,87,96,163,165]. More specifically, the use of neurostimulation BCIs in clinical environments introduces severe vulnerabilities that can have a significant impact on the user's health condition [136]. ...
... BCIs already existing on the market would benefit from the implementation of robust security solutions, reducing their impact, particularly in clinical environments. Furthermore, the expansion of BCIs to new markets, e.g., video games or entertainment, generates considerable risks in terms of data confidentiality [87,96,163,165]. In this context, users' personal information, such as thoughts, emotions, sexual orientation, or religious beliefs, are under threats if security measures are not adopted [59,96,165]. ...
... Furthermore, the expansion of BCIs to new markets, e.g., video games or entertainment, generates considerable risks in terms of data confidentiality [87,96,163,165]. In this context, users' personal information, such as thoughts, emotions, sexual orientation, or religious beliefs, are under threats if security measures are not adopted [59,96,165]. Besides, contemporary BCI approaches, such as the use of silicon-based interfaces, introduce new security challenges due to the increase in the volume of acquired data and the use of potentially vulnerable technology [121]. ...
Brain-Computer Interfaces (BCIs) have significantly improved the patientsfi quality of life by restoring damaged hearing, sight, and movement capabilities. After evolving their application scenarios, the current trend of BCI is to enable new innovative brain-to-brain and brain-to-the-Internet communication paradigms. This technological advancement generates opportunities for attackers since users’ personal information and physical integrity could be under tremendous risk. This work presents the existing versions of the BCI life-cycle and homogenizes them in a new approach that overcomes current limitations. After that, we offer a qualitative characterization of the security attacks affecting each phase of the BCI cycle to analyze their impacts and countermeasures documented in the literature. Finally, we reflect on lessons learned, highlighting research trends and future challenges concerning security on BCIs.
... Copyrights for components of this work owned by Neuroprivacy concerns represent a unique and pressing challenge for privacy professionals as the mind becomes ever more connected and discernible with advances in neurotechnology. There is uncertainty as to whether data protection regimes can adequately address neuroprivacy concerns [20], and other scholars have identified a lack of basic privacy protections such as applications having excessive access to personal information in popular BCI headsets [39]. There was one application that sent users' raw EEG data to cloud storage, potentially allowing unknown parties to extract sensitive personal information from users' brainwaves at some point in the future. ...
... This tool has been proposed as a way to remove private information from EEG data before they are stored or transmitted [5], although it has not been invented as suggested by the abandoned status of the BCI Anonymizer patent application [9]. Several issues with the BCI Anonymizer idea have been identified, including resource constraints in BCI devices, lack of access to proprietary algorithms, lack of a clear method for separating private information from intentions and a general lack of any implementation details [39]. ...
... Adding noise to EEG data before applications can process it could decrease the risk of personal data leaks [28]. Differential privacy is a specific application of noise addition that could be deployed in BCIs to deidentify brainwaves [39]. Personal Data Stores could be equipped with capabilities for aggregate computation of neural data across multiple PDS instances and summarizing EEG data into high-level attributes by reducing the dimensionality of the data [37]. ...
... Therefore, different companies have tried to solve the security issues and functional design problems by using common standards. The inability to alter or add functions as required is also a disadvantage of BCI devices [18,19,20,21]. ...
... All aspect mentioned above affect the solutions to the applicable network security. This is because the BCI system only considers the defense mechanism of possible threats, but for some other known threats, expansion can't be realized by adding the threat defense function, which, to a certain extent, will cause many unpredictable security problems in the BCI system [20] [21]. A possible solution to this problem is BCI modular design, that is, adding different modules to improve the overall function according to different J o u r n a l P r e -p r o o f information [21]. ...
Brain Computer Interface BCI is a real-time communication system that connects the brain and external devices. The BCI system can directly convert the information sent by the brain into com- mands that can drive external devices and replace human limbs or language organs to achieve hu- man communication with the outside world and the external environment Control. In other words, the BCI system can replace the normal peripheral nerve and muscle tissue to achieve communi- cation between the human and the computer or between the human and the external environment. The objective of this paper is to assist the network security for the BCI applications to identify brain activities in a secure real-time mode.To achieve this, we proposed the design of a Radio Fre- quency Identification RFID-based system having semi− active RFID tags placed outside the brain on the scalp transmit the collected brain activities wirelessly to a devise SC (Scanner Controller) consist of mini-reader and timer integrated together for every patient. Additionally, the paper im- plemented a novel system prototype interface called BCI Identification System (BCIIS) to assist the patient in the identification process. Given the benefits of RFID, we believed that if the idea is adopted and implemented by industry, it could enhance and provide secure BCI applications.
... Platforms and frameworks that enable the development of BCI applications also present cybersecurity concerns, as demonstrated in [18], [19]. In this context, the authors of [18] performed an analysis of the privacy concerns of BCI application stores, including Software Development Kits (SDKs), Application Programming Interfaces (APIs), and BCI applications. ...
... Platforms and frameworks that enable the development of BCI applications also present cybersecurity concerns, as demonstrated in [18], [19]. In this context, the authors of [18] performed an analysis of the privacy concerns of BCI application stores, including Software Development Kits (SDKs), Application Programming Interfaces (APIs), and BCI applications. They discovered that most applications have unrestricted access to subjects' brainwave signals and can easily extract private information about their subjects. ...
Brain-Computer Interfaces (BCI) arose as systems that merge computing systems with the human brain to facilitate recording, stimulation, and inhibition of neural activity. Over the years, the development of BCI technologies has shifted towards miniaturization of devices that can be seamlessly embedded into the brain and can target single neuron or small population sensing and control. We present a motivating example highlighting vulnerabilities of two promising micron-scale BCI technologies, demonstrating the lack of security and privacy principles in existing solutions. This situation opens the door to a novel family of cyberattacks, called neuronal cyberattacks, affecting neuronal signaling. This paper defines the first two neural cyberattacks, Neuronal Flooding (FLO) and Neuronal Scanning (SCA), where each threat can affect the natural activity of neurons. This work implements these attacks in a neuronal simulator to determine their impact over the spontaneous neuronal behavior, defining three metrics: number of spikes, percentage of shifts, and dispersion of spikes. Several experiments demonstrate that both cyberattacks produce a reduction of spikes compared to spontaneous behavior, generating a rise in temporal shifts and a dispersion increase. Mainly, SCA presents a higher impact than FLO in the metrics focused on the number of spikes and dispersion, where FLO is slightly more damaging, considering the percentage of shifts. Nevertheless, the intrinsic behavior of each attack generates a differentiation on how they alter neuronal signaling. FLO is adequate to generate an immediate impact on the neuronal activity, whereas SCA presents higher effectiveness for damages to the neural signaling in the long-term.
... Takabi et al. (2016) explored privacy threats in BCI applications, highlighting concerns such as unauthorized access to sensitive brain data. They proposed various countermeasures, including anonymization techniques, robust access control mechanisms, and encryption to safeguard the data [23]. Wahlstrom et al. (2016) focused on privacy disruptions across different BCI systems, identifying key issues like confidentiality attacks where sensitive information could be leaked, and data availability challenges that might hinder the reliable functioning of BCIs [24]. ...
Brain-computer interfaces (BCIs) hold immense promise for human benefits, enabling communication between the brain and computer-controlled devices. Despite their potential, BCIs face significant cybersecurity risks, particularly from Bluetooth vulnerabilities. This study investigates Bluetooth vulnerabilities in BCIs, analysing potential risks and proposing mitigation measures. Various Bluetooth attacks such as Bluebugging, Bluejacking, Bluesnarfing, BlueBorne, Location Tracking, Man-in-the-Middle Attack, KNOB, BLESA and Reflection Attack are explored, along with their potential consequences on commercial BCI systems. Each attack is examined in terms of its modus operandi and effective mitigation strategies.
... Frank et al. [12] and Quiles Pérez et al. [13] followed a similar approach, studying the duration of the stimuli presented, aiming to ensure that the subjects were not aware of the attack, thus studying the subliminal threshold and demonstrating the effectiveness of stimuli almost invisible to the human eye. Takabi et al. [18] studied the most common BCI applications, identifying that most of this software can access brain data without restrictions, affecting the confidentiality of its users' sensitive data. Finally, Bonaci et al. [14] presented the concept of BCI Anonymizer as a layer added to these devices, able to anonymize the signals transmitted from the BCI to external systems, guaranteeing the confidentially of information against BCI applications offering unlimited access to neural data. ...
Brain-computer interfaces (BCIs) are widely used in medical scenarios to treat neurological conditions, such as Parkinson’s disease or epilepsy, when a pharmacological approach is ineffective. Despite their advantages, these BCIs target relatively large areas of the brain, causing side effects. In this context, projects such as Neuralink aim to stimulate and inhibit neural activity with single-neuron resolution, expand their usage to other sectors, and thus democratize access to neurotechnology. However, these initiatives present vulnerabilities in their designs that cyberattackers can exploit to cause brain damage. Specifically, the literature has documented the applicability of neural cyberattacks, threats capable of stimulating or inhibiting individual neurons to alter spontaneous neural activity. However, these works were limited by a lack of realistic neuronal topologies to test the cyberattacks. Surpassed this limitation, this work considers a realistic neuronal representation of the primary visual cortex of mice to evaluate the impact of neural cyberattacks more realistically. For that, this publication evaluates two existing cyberattacks, Neuronal Flooding and Neuronal Jamming, assessing the impact that different voltages on a particular set of neurons and the number of neurons simultaneously under attack have on the amount of neural activity produced. As a result, both cyberattacks increased the number of neural activations, propagating their impact for approximately 600 ms, where the activity converged into spontaneous behavior. These results align with current evidence about the brain, highlighting that neurons will tend to their baseline behavior after the attack.
... They analyze the different building blocks of BCIs and study how each of them could be attacked. Some of this related research also proposes countermeasures to prevent such attacks or strategies to mitigate the risks involved [29][30][31][32][33][34][35][36]. ...
... In the article by Luigi Bianchi, 11 the author informs lack of specific standards that govern development of BCI applications. This challenge, as noted by Takabi et al. [109], has resulted in BCI applications with unrestricted access to brain signals. The authors' results show that these applications may, as a consequence, extract sensitive information from users without their knowledge. ...
Brain–computer interface (BCI), an emerging technology that facilitates communication between brain and computer, has attracted a great deal of research in recent years. Researchers provide experimental results demonstrating that BCI can restore the capabilities of physically challenged people, hence improving the quality of their lives. BCI has revolutionized and positively impacted several industries, including entertainment and gaming, automation and control, education, neuromarketing, and neuroergonomics. Notwithstanding its broad range of applications, the global trend of BCI remains lightly discussed in the literature. Understanding the trend may inform researchers and practitioners on the direction of the field, and on where they should invest their efforts more. Noting this significance, we have analyzed 25,336 metadata of BCI publications from Scopus to determine advancement of the field. The analysis shows an exponential growth of BCI publications in China from 2019 onwards, exceeding those from the United States that started to decline during the same period. Implications and reasons for this trend are discussed. Furthermore, we have extensively discussed challenges and threats limiting exploitation of BCI capabilities. A typical BCI architecture is hypothesized to address two prominent BCI threats, privacy and security, as an attempt to make the technology commercially viable to the society.
... This technology is moving into the commercial market, with wearable brainstimulation devices for non-medical purposes, such as relaxation, fitness, mood or physical strength enhancement, meditation, and concentration [186][187][188][189][190][191][192]. These devices may be considered as an upcoming part of the IoE in a fully connected environment designed to allow platforms and apps to interact with them, allowing two-way personal (brain)-data gathering and analysis [193,194]. Such devices may not only influence an individual's brain activity, but also trace its dynamic processes and related brain data [195]. ...
Global concern about problematic usage of the internet (PUI), and its public health and societal costs, continues to grow, sharpened in focus under the privations of the COVID-19 pandemic. This narrative review reports the expert opinions of members of the largest international network of researchers on PUI in the framework of the European Cooperation in Science and Technology (COST) Action (CA 16207), on the scientific progress made and the critical knowledge gaps remaining to be filled as the term of the Action reaches its conclusion.
A key advance has been achieving consensus on the clinical definition of various forms of PUI. Based on the overarching public health principles of protecting individuals and the public from harm and promoting the highest attainable standard of health, the World Health Organisation has introduced several new structured diagnoses into the ICD-11, including gambling disorder, gaming disorder, compulsive sexual behaviour disorder, and other unspecified or specified disorders due to addictive behaviours, alongside naming online activity as a diagnostic specifier. These definitions provide for the first time a sound platform for developing systematic networked research into various forms of PUI at global scale. Progress has also been made in areas such as refining and simplifying some of the available assessment instruments, clarifying the underpinning brain-based and social determinants, and building more empirically based etiological models, as a basis for therapeutic intervention, alongside public engagement initiatives.
However, important gaps in our knowledge remain to be tackled. Principal among these include a better understanding of the course and evolution of the PUI-related problems, across different age groups, genders and other specific vulnerable groups, reliable methods for early identification of individuals at risk (before PUI becomes disordered), efficacious preventative and therapeutic interventions and ethical health and social policy changes that adequately safeguard human digital rights. The paper concludes with recommendations for achievable research goals, based on longitudinal analysis of a large multinational cohort co-designed with public stakeholders.
... Brain spyware [42] presents a malicious software to communicate visual stimuli and steal private information (e.g., 4-digit PINs, or location of residence) using a BCI to record the EEG signals. Takabi et al. [62] highlighted inadequate privacy considerations in the NeuroSky and Emotive APIs. More recently, other research efforts explored adversarial attacks on EEG-based models [45,72,55]. ...
Brain computing interfaces (BCI) are used in a plethora of safety/privacy-critical applications, ranging from healthcare to smart communication and control. Wearable BCI setups typically involve a head-mounted sensor connected to a mobile device, combined with ML-based data processing. Consequently, they are susceptible to a multiplicity of attacks across the hardware, software, and networking stacks used that can leak users' brainwave data or at worst relinquish control of BCI-assisted devices to remote attackers. In this paper, we: (i) analyse the whole-system security and privacy threats to existing wearable BCI products from an operating system and adversarial machine learning perspective; and (ii) introduce Argus, the first information flow control system for wearable BCI applications that mitigates these attacks. Argus' domain-specific design leads to a lightweight implementation on Linux ARM platforms suitable for existing BCI use-cases. Our proof of concept attacks on real-world BCI devices (Muse, NeuroSky, and OpenBCI) led us to discover more than 300 vulnerabilities across the stacks of six major attack vectors. Our evaluation shows Argus is highly effective in tracking sensitive dataflows and restricting these attacks with an acceptable memory and performance overhead (<15%).
... Hassan Takabi Joshi et. al., [5] perform the first complete investigation of BCI App stores in points of privacy considerations, encompassing software development kits (SDKs), application programming interfaces (APIs), and BCI programs. The purpose is to learn how BCI applications manage brainwave signals and what concerns to users' security. ...
A Calibration technique is a method used brain computer interface (BCI) system, which requires a time period of 20-30 minutes. The procedure of calibration is problematic and unfeasible for building the reliable decoder. To overcome the drawback of existing system, a spectral-spatial algorithm is proposed. The data set of motor imagery (MI) which consists of 14 subjects and 15 electroencephalography (EEG) signals is taken into considerations. The two modules are constructed for data preprocessing and feature extraction. The proposed spectral-spatial algorithm is independently trained and test through artificial neural network (ANN). Based on that classification is done using several machine learning approaches like random forest (RF), neural network (NN), XGboost and given as incoming to hidden layer (Lth layer). The obtained results indicates 2% of improvement in comparison with existing methodology.
... Although previous studies have highlighted the applicability of cryptographic and jam-ming attacks ( Ienca and Haselager, 2016 ), malware strategies ( Bonaci et al., 2015 ), acquisition of sensitive data from neural signals , disruption of neural signals , or potential attacks over BCI architectures ( Ballarin Usieto and Minguez, 2018 ), these works are scarce and focus on particular privacy and security aspects, not addressing the physical safety dimension. Additionally, the authors of Takabi et al. (2016) , Bonaci et al. (2015) identified that the platforms and frameworks used to develop BCI applications could be vulnerable to cyberattacks. Based on that, the authors of Bernal et al. (2021) performed a review of the state of the art in cybersecurity on BCI with a comprehensive analysis of physical safety issues, compiling already documented attacks over the BCI life-cycle, their impacts, and the countermeasures to detect and mitigate them. ...
Invasive Brain-Computer Interfaces (BCIs) are extensively used in medical application scenarios to record, stimulate, or inhibit neural activity with different purposes. An example is the stimulation of some brain areas to reduce the effects generated by Parkinson’s disease. Despite the advances in recent years, cybersecurity on BCIs is an open challenge since attackers can exploit the vulnerabilities of invasive BCIs to induce malicious stimulation or treatment disruption, affecting neuronal activity. In this work, we design and implement a novel neuronal cyberattack called Neuronal Jamming (JAM), which prevents neurons from producing spikes. To implement and measure the JAM impact, and due to the lack of realistic neuronal topologies in mammalians, we have defined a use case using a Convolutional Neural Network (CNN) trained to allow a simulated mouse to exit a particular maze. The resulting model has been translated to a biological neural topology, simulating a portion of a mouse’s visual cortex. The impact of JAM on both biological and artificial networks is measured, analyzing how the attacks can both disrupt the spontaneous neural signaling and the mouse’s capacity to exit the maze. Besides, another contribution of the work focuses on comparing the impacts of both JAM and FLO (an existing neural cyberattack), demonstrating that JAM generates a higher impact in terms of neuronal spike rate. As a final contribution, we discuss whether and how JAM and FLO attacks could induce the effects of neurodegenerative diseases if the implanted BCI had a comprehensive electrode coverage of the targeted brain regions.
... Individual cortical neurons respond to stimuli only in a restricted region of the visual field known as the receptive field. The receptive fields of different neurons partially overlap such that they cover the entire visual field, Takabi et al. (2016). ...
This paper output is a music player application but when it comes to its features it will be way more than a simple music player. It is developed on Android Studio and other tools like: Firebase is used as database, Android phone camera, Music library of Android Phone are used in the development of application. When user changes his phone or reset his phone then all of his data is lost or user has to put all the data in his computer and then back to his mobile phone except data that is backed up online. Message data, photos and contacts are that things that users backed up online. But music files normally don’t get backed up and user troubles in re downloading the files or moving files in computer and back to phone. In this purposed work the targeted problem is resolved as MUSYNC application is be able to automatically backup all the mp3 data from the phone and user will get all of his data by just signing in the application in his new phone. The purposed application has a feature of sync music. Users can sync music with another one and that person will able to listen to same music instantly. Application also provides a unique feature of mood detection using digital image processing DIP. This feature is able to check your face emotion and play music according to it. User just has to take a picture and that is it, this music player plays the music according to your mood. This feature is useful when user having tough time what to listen.
... could maliciously add or modify software modules defining the BCI to perform dangerous actions over the users. Finally, Takabi et al. [14] highlighted that most APIs used to develop BCI applications offered complete access over the information acquired by the BCI, presenting confidentiality problems. ...
This article presents eight neural cyberattacks affecting spontaneous neural activity, inspired by well-known cyberattacks from the computer science domain: Neural Flooding, Neural Jamming, Neural Scanning, Neural Selective Forwarding, Neural Spoofing, Neural Sybil, Neural Sinkhole and Neural Nonce. These cyberattacks are based on the exploitation of vulnerabilities existing in the new generation of Brain-Computer Interfaces. After presenting their formal definitions, the cyberattacks have been implemented over a neuronal simulation. To evaluate the impact of each cyberattack, they have been implemented in a Convolutional Neural Network (CNN) simulating a portion of a mouse's visual cortex. This implementation is based on existing literature indicating the similarities that CNNs have with neuronal structures from the visual cortex. Some conclusions are also provided, indicating that Neural Nonce and Neural Jamming are the most impactful cyberattacks for short-term effects, while Neural Scanning and Neural Nonce are the most damaging for long-term effects.
... The Recommendations for Responsible Development and Application of Neurotechnologies concept of BCI "App Stores" has been implemented by some neurotechnology companies including Emotiv and NeuroSky to expand BCI applications. Most of the applications included are granted unrestricted access to users' raw electroencephalogram (EEG) signals [53]. In 2012, Martinovic presented "brain spyware," which can extract confidential information about an individual via a BCI-enabled malicious application [54]. ...
Advancements in novel neurotechnologies, such as brain computer interfaces (BCI) and neuromodulatory devices such as deep brain stimulators (DBS), will have profound implications for society and human rights. While these technologies are improving the diagnosis and treatment of mental and neurological diseases, they can also alter individual agency and estrange those using neurotechnologies from their sense of self, challenging basic notions of what it means to be human. As an international coalition of interdisciplinary scholars and practitioners, we examine these challenges and make recommendations to mitigate negative consequences that could arise from the unregulated development or application of novel neurotechnologies. We explore potential ethical challenges in four key areas: identity and agency, privacy, bias, and enhancement. To address them, we propose (1) democratic and inclusive summits to establish globally-coordinated ethical and societal guidelines for neurotechnology development and application, (2) new measures, including “Neurorights,” for data privacy, security, and consent to empower neurotechnology users’ control over their data, (3) new methods of identifying and preventing bias, and (4) the adoption of public guidelines for safe and equitable distribution of neurotechnological devices.
The brain-computer interface (BCI) is a growing field of technology, and it has become clear that BCI systems’ cybersecurity needs amelioration. When BCI devices are developed with wireless connection capabilities, more often than not, this creates more surface area for attackers to concentrate their attacks. The more invasive BCI technology is used, the greater the threat to the users’ physical health. In this paper, we summarize and outline the main cybersecurity threats and challenges that BCI systems may face now and in the future. Furthermore, we present avenues for the future BCI systems including cybersecurity solutions and requirements. We emphasize the importance of the health layer to be considered as important as technical layers in BCI systems as people cannot endure life-threatening situations where attackers could cause permanent brain damage to the BCI user.KeywordsBrain-Computer InterfaceDeep Brain StimulationCybersecurityVulnerabilityPrivacy
Brain–Computer Interface (BCI) technology is a promising research area in many domains. Brain activity can be interpreted through both invasive and noninvasive monitoring devices, allowing for novel, therapeutic solutions for individuals with disabilities and for other non-medical applications. However, a number of ethical issues have been identified from the use of BCI technology. In previous work published in 2020, we reviewed the academic discussion of the ethical implications of BCI technology in the previous 5 years by using a limited sample to identify trends and areas of concern or debate among researchers and ethicists. In this chapter, we provide an overview on the academic discussion of BCI ethics and report on the findings for the next phase of this work, which systematically categorizes the entire sample. The aim of this work is to collect and synthesize all the pertinent academic scholarship into the ethical, legal, and social implications (ELSI) of BCI technology. We hope this study will provide a foundation for future scholars, ethicists, and policy makers to understand the landscape of the relevant ELSI concepts and pave the way for assessing the need for regulatory action. We conclude that some emerging applications of BCI technology—including commercial ventures that seek to meld human intelligence with AI—present new and unique ethical concerns.KeywordsBrain–computer interface (BCI)Brain–machine interface (BMI)Ethical, legal, and social issues (ELSI)NeuroethicsScoping review
Cyberbiosecurity is an emerging field that brings together diverse professionals, including biologists, computer scientists, anti-terrorism experts, and policy makers to research the growing intersection between cybersecurity and the biosciences. Cyberneurosecurity is the nascent subfield that is particularly focused on the issues related to neuroscience and cybersecurity. Internet-enabled Brain–Computer Interfaces (BCIs) like the futuristic Neuralink Link devices (Neuralink, 2023) which is expected to be on the market within a decade create numerous ethical and policy issues that are one of the chief concerns of cyberneurosecurity.These issues can relate to (1) privacy and misappropriation resulting from the interception of neural signals that could disclose behaviors and inclinations; (2) the inoperability of associated devices like prosthetics that could result from the obfuscation or manipulation of neural signals; (3) the potential physical and cognitive and existential harms that result from receiving hacked signals in the brain and/or the hijacking of neural signals sent from the brain for medical purposes; or (4) self-hacking by the user themselves for their own putative benefits.These and others are issues that cyberneurosecurity must engage. In response to these concerns, researchers need to devise standards, policies, and best practices to prevent malicious hackers from manipulating the technology. Practitioners need to develop tools to stress-test and assess the cyber-readiness of various BCIs, especially the increasing number of healthcare devices that employ AI that could obscure or magnify harmful hacks due in part to the lack of transparency and explainability of AI systems (Zhang et al., Ann Transl Med 8(11):712, 2020, Olsen et al., J Neural Eng 18(4):046053, 2021, Aggarwal and Chugh, Arch Comput Methods Eng 29:3001–20, 2022). BCI manufacturers need to ultimately implement industry-wide standards to protect the privacy, security, and safety of their users, and governments may need to develop regulatory oversight to promote these and other aspects of cyberneurosecurity.KeywordsCyberbiosecurityCyberneurosecurityNeurorightsNeurohypeBrain–computer interfaces (BCI)
Brain–Computer Interface (BCI) technology is a promising and rapidly advancing research area. It was initially developed in the context of early government-sponsored futuristic research in biocybernetics and human–machine interaction in the United States (US) [1]. This inspired Jacques Vidal to suggest providing a direct link between the inductive mental processes used in solving problems and the symbol-manipulating, deductive capabilities of computers, and to coin the term “Brain-Computer Interface” in his seminal paper published in 1973 [2]. Recent developments in BCI technology, based on animal and human studies, allow for the restoration and potential augmentation of faculties of perception and physical movement, and even the transfer of information between brains. Brain activity can be interpreted through both invasive and noninvasive monitoring devices, allowing for novel, therapeutic solutions for individuals with disabilities and for other non-medical applications. However, a number of ethical and policy issues have been identified in context of the use of BCI technology, with the potential for near-future advancements in the technology to raise unique new ethical and policy questions that society has never grappled with before [3, 4]. Once again, the US is leading in the field with many commercial enterprises exploring different realistic and futuristic applications of BCI technology. For instance, a US company named Synchron recently received FDA approval to proceed with first-in-human trials of its endovascularly implanted BCI device [5].
Defining and analyzing the impact of cyberattacks on novel generations of BCIs.
A brain–computer interface (BCI) establishes a direct communication pathway between the human brain and a computer. It has been widely used in medical diagnosis, rehabilitation, education, entertainment, and so on. Most research so far focuses on making BCIs more accurate and reliable, but much less attention has been paid to their privacy. Developing a commercial BCI system usually requires close collaborations among multiple organizations, e.g., hospitals, universities, and/or companies. Input data in BCIs, e.g., electroencephalogram (EEG), contain rich privacy information, and the developed machine learning model is usually proprietary. Data and model transmission among different parties may incur significant privacy threats, and hence, privacy protection in BCIs must be considered. Unfortunately, there does not exist any contemporary and comprehensive review on privacy-preserving BCIs. This article fills this gap, by describing potential privacy threats and protection strategies in BCIs. It also points out several challenges and future research directions in developing privacy-preserving BCIs.
Brain-Computer Interfaces (BCIs) have experienced a considerable evolution in the last decade, expanding from clinical scenarios to sectors such as entertainment or video games. Nevertheless, this popularization makes them a target for cyberattacks like malware. Current literature lacks comprehensive works focusing on cybersecurity applied to BCIs and, mainly, publications performing a rigorous analysis of the risks and weaknesses that these interfaces present. If not studied properly, these potential vulnerabilities could dramatically impact users' data, service availability, and, most importantly, users' safety. Because of that, this work introduces an evaluation of the risk that each BCI classification already defined in the literature presents to raise awareness between the readers of this chapter about the potential threat that BCIs can generate in the next years if comprehensive measures, based on standard mechanisms, are not adopted. Moreover, it seeks to alert academic and industrial stakeholders about the impact these risks could have on future BCI hardware and software.
Brain-computer interfaces are considered as the next level of human-machine interaction. A bunch of approaches in decoding human states aims to achieve sufficient precision and accommodate a growing number of distinct states to decode. The following study investigates the capabilities of the EEG-based inverse modelling to improve the classification accuracy and provides a comparison between different inverse models. The computational pipeline of represented BCI includes clustering and dimension reduction of the forward model. The obtained results show the advantages of minimal norm estimate (MNE) inverse operator in comparison to the Beamformer, sLORETA. We have also observed that a motor imagery BCI based on the fully blown individual inverse model outperformed that based on Riemann geometry-based approaches, while the latter demonstrated performance superior to the approaches using band specific sensor space power distribution. The performance analysis was done using a 32 channel EEG data recorded during motor imagery of the four limbs.
Profiling activities used to personalise user content can place online corporations in a position of significant influence over user behaviour. The ability to deliver different search engine results, news feeds, discriminatory prices, and so forth, can be exploited to grant unequal access to different kinds of content. This is likely to intensify with the Internet of Things (IoT), which will surround individuals with sensors capable of 24/7 full tracking. This content-shaping power jeopardises the neutral accessibility that a democratic internet should ensure. Indeed, both neutrality and accessibility should refer not only to the service connection but also to the content it delivers. This study examines this personalisation and filtering power in light of Privacy and Data Protection regulations to explore how privacy principles can be used to enhance IoT accessibility. It elaborates a comparative analysis of legislative sources related to public goods and interests, data flows and contract law relationships, into a holistic narrative reasoning. The discussion addresses the monopolistic economic position of platforms, their connected revenue models and the power to control and influence information. Fake news plays a crucial role in this and is addressed consequently. Therefore, the study analyses how these factors together could form profiling ‘multi-monopolies’ in connection with a non-neutral Internet of Things. Furthermore, techniques of influence have more grip where there is a lack of awareness and knowledge, and a new model of digital “divide et impera” seems to enhance these phenomena. Besides, social score systems and discriminatory pricing can be powerful tools in this sense. We discuss the gap between regulatory approaches and real-world situations, offering common legal tools and Privacy by Design (PbD) as potential solutions for neutral accessibility. We highlight how the current regulatory framework requires more harmonisation to guarantee effective user protections, especially in an IoT ecosystem.
In this chapter, a number of key ethical issues associated with the recent emergence of the related fields of neuromarketing and sensory marketing are reviewed. Now that these new techniques are really starting to show their predictive mettle relative to other, more traditional, consumer psychology/behavioural testing approaches to marketing, questions around the ethics of stimulating the brain’s “buy button” start to raise their head. Here, I want to question what exactly is so special, and so worrying, about “looking inside the mind of the consumer”. I will argue that public fears around the dangers of neuromarketing have been overblown, at least up until the present time and, as far as I can see, for the foreseeable future. I do, though, want to raise a number of concerns around the growing influence of sensory marketing on our behaviour, focusing, in particular, on the world of food and drink marketing. Ultimately, I believe that the consumer of tomorrow may well have much to fear from the emerging neuroscience-inspired approaches to sensory marketing. In fact, before too long, we may all start to find ourselves being sensorially “nudged” into a range of less healthy food behaviours. As such, establishing clear ethical guidelines will, I believe, become an increasingly important issue for those working in the field.
Neuroenhancement is associated with a wide range of existing, emerging, and future biomedical technologies that are intended to improve human cognitive performance and mitigate—if not reverse—human error. Neuroenhancement in classrooms, universities, and the military has been discussed at length, but the workplace has been largely omitted from the conversation until now. By providing examples from branches of the commercial market that are rarely linked with cognitive enhancement in the literature, we argue that neuroenhancement at work is likely to become a major challenge in the labor market. Therefore, we focus here on the specific application of neuroenhancements to the workplace. Central issues involve both drugs and devices, some of which are well-trodden ethical concerns while others are novel challenges. We conclude with a brief discussion and outline of a discourage-use policy that has the potential to mitigate the challenges of neuroenhancement at work.
In this chapter, we offer a content analysis of top-tier management journals to examine the extent to which advocates of neuroscience in management pay heed to the ethical ramifications of their work. Based upon our analysis, we are able to robustly refute the claim by Butler and colleagues (Hum Relat 70:1171–1190, 2017) that Lindebaum’s (Hum Relat 69(3):537–50, 2016) concerns about the lack of ethical concerns in the proliferation and application of neuroscientific ideas and measurements are basically much ado about nothing. By way of this content analysis, we advance the debate on the ethical ramifications of applying neuroscience in management by demonstrating (1) which ethical issues are recognised and (2) which ones are not. Doing so has the potential to open up new directions in studying the ethical and practical ramifications of neuroscience in and around workplaces.
The commercialization of EEG devices (electroencephalography) increases the possibility of their use in different fields other than medicine. These brain computer interfaces (BCI) can be a solution for people who suffer from sever motor disabilities like amyotrophic lateral sclerosis (ALS). It offer them the ability to control a computer without any movement. In fact, the EEG signals are transformed into machine commands. Thus, it exist deferent BCI based systems. Our main objective here, is to offer a video surveillance system commanded by voluntary blinks. This voluntary blinks are detected from the EEG signals using our blink detection approach. It’s based on unsupervised clustering of the blink regression parameters using the Gaussian mixture model (GMM). Based on the user tests of our system, it was well appreciated.
There is no currently known systematic and quantitative procedure developed to design smart workplace environments through guiding efficient and effective adoption and exploitation of the currently available wide variety of smart-environment-enabling technologies. This article presents a novel methodology to design a smart workplace environment objectively to improve, for example, productivity and safety. The proposed methodology starts by analysing the given subject workplace environment in terms of tasks and goals, and then structures and quantifies effect relations among workplace overall goals and the smart-environment-enabling technologies through linking them to the intermediate task objectives, potential improvement opportunities and smart characteristics affected by those smart technologies. Fuzzy relations are utilized to express vague effect relations among related goals, objectives, concepts and technology elements of the proposed design model. The Max-Min composition is used to compose relations to obtain the final overall goal-technology relationships. The methodology eventually yields a composed relation among workplace overall performance goals and the investigated smart-environment-enabling technologies. This overall composed relation reveals promising enabling technologies that are relevant to achievement of the specified goals. Identification of goals promising smart technologies can be used directly as a guide to realize a goal-based smart workplace environment design. Promising technology relations to task goals also help to deploy those promising technologies to the relevant tasks. A comprehensive example case is used to demonstrate the applicability of the methodology.
Recently, studies on different applications using measured brain waves have been increasingly progressing. It started out with studying brain waves of a monkey to control an arm with a motor. Then it is applied for raising children's attention or understanding a patient situation. This paper deals with measuring the attention and meditation of EEG signal when a smartphone game user is playing. A survey will be conducted that measures the EEG signals for the smartphone game users while playing the game for analysis. The results will be depending on the memory and expressions of game users when solving hard problems during the game.
This study investigates dynamic time warping
(DTW) as a possible analysis method for EEG-based affective
computing in a self-paced learning task in which inter- and intrapersonal
differences are large. In one experiment, participants
(N=200) carried out an implicit category learning task where
their frontal EEG signals were collected throughout the
experiment. Using DTW, we measured the dissimilarity distances
of EEG signals between participants and examined the extent to
which a k-Nearest Neighbors algorithm could predict self-rated
feelings of a participant from signals taken from other
participants (between-participants prediction). Results showed
that DTW provides potentially useful characteristics for EEG
data analysis in a heterogeneous setting. In particular, theory-based segmentation of time-series data were particularly useful for DTW analysis while smoothing and standardization were detrimental when applied in a self-paced learning task.
Brain-Computer Interfaces (BCI) are becoming increasingly popular in medical and non-medical areas. Unfortunately , manufacturers of BCI devices focus on application development, without paying much attention to security and privacy related issues. Indeed, an increasing number of attacks to BCI applications underline the existence of such issues. For example, malicious developers of third-party applications could extract private information of users. In this paper, we focus on security and privacy of BCI applications. In particular, we classify BCI applications into four usage scenarios: 1) neuromedical applications, 2) user authentication, 3) gaming and entertainment, and 4) smartphone-based applications. For each usage scenario, we discuss security and privacy issues and possible countermeasures.
This paper provides a proof of concept for an EEG-based reconstruction of a visual image which is on a user's mind. Our approach is based on the Rapid Serial Visual Presentation (RSVP) of polygon primitives and Brain-Computer Interface (BCI) technology. The presentation of polygons that contribute to build a target image (because they match the shape and/or color of the target) trigger attention-related EEG patterns. Accordingly, these target primitives can be determined using BCI classification of Event-Related Potentials (ERPs). They are then accumulated in the display until a satisfactory reconstruction is reached. Selection steps have an average classification accuracy of 75%. 25% of the images could be reconstructed completely, while more than 65% of the available visual details could be captured on average. Most of the misclassifications were not misinterpretations of the BCI concerning users' intent; rather, users tried to select polygons that were different than what was intended by the experimenters. Open problems and alternatives to develop a practical BCI-based image reconstruction application are discussed.
Brain computer interfaces (BCI) are becoming increasingly popular in the gaming and entertainment industries. Consumer-grade BCI devices are available for a few hundred dollars and are used in a variety of applications, such as video games, hands-free keyboards, or as an assistant in relaxation training. There are application stores similar to the ones used for smart phones, where application developers have access to an API to collect data from the BCI devices. The security risks involved in using consumer-grade BCI devices have never been studied and the impact of malicious software with access to the device is unexplored. We take a first step in studying the security implications of such devices and demonstrate that this upcoming technology could be turned against users to reveal their private and secret information. We use inexpensive electroencephalography (EEG) based BCI devices to test the feasibility of simple, yet effective, attacks. The captured EEG signal could reveal the user's private information about, e.g., bank cards, PIN numbers, area of living, the knowledge of the known persons. This is the first attempt to study the security implications of consumer-grade BCI devices. We show that the entropy of the private information is decreased on the average by approximately 15%-40% compared to random guessing attacks.
Martinovic et al. proposed a Brain-Computer-Interface (BCI) -based attack in
which an adversary is able to infer private information about a user, such as
their bank or area-of-living, by analyzing the user's brain activities.
However, a key limitation of the above attack is that it is intrusive,
requiring user cooperation, and is thus easily detectable and can be reported
to other users. In this paper, we identify and analyze a more serious threat
for users of BCI devices. We propose a it subliminal attack in which the victim
is attacked at the levels below his cognitive perception. Our attack involves
exposing the victim to visual stimuli for a duration of 13.3 milliseconds -- a
duration usually not sufficient for conscious perception. The attacker analyzes
subliminal brain activity in response to these short visual stimuli to infer
private information about the user. If carried out carefully, for example by
hiding the visual stimuli within screen content that the user expects to see,
the attack may remain undetected. As a consequence, the attacker can scale it
to many victims and expose them to the attack for a long time. We
experimentally demonstrate the feasibility of our subliminal attack via a
proof-of-concept study carried out with 27 subjects. We conducted experiments
on users wearing Electroencephalography-based BCI devices, and used portrait
pictures of people as visual stimuli which were embedded within the background
of an innocuous video for a time duration not exceeding 13.3 milliseconds. Our
experimental results show that it is feasible for an attacker to learn relevant
private information about the user, such as whether the user knows the identity
of the person for which the attacker is probing.
The neuroscience revolution poses profound challenges to current self-incrimination doctrine and exposes a deep conceptual confusion at the heart of the doctrine. In Schmerber v. California, the Court held that under the Self- Incrimination Clause of the Fifth Amendment, no person shall be compelled to “prove a charge [from] his own mouth,” but a person may be compelled to provide real or physical evidence. This testimonial/physical dichotomy has failed to achieve its intended simplifying purpose. For nearly fifty years scholars and practitioners have lamented its impracticability and its inconsistency with the underlying purpose of the privilege. This Article seeks to reframe the debate. It demonstrates through modern applications from neuroscience the need to redefine the taxonomy of evidence subject to the privilege against self-incrimination. Evidence can arise from the identifying characteristics inherent to individuals; it can arise automatically, without conscious processing; it can arise through memorialized photographs, papers, and memories; or it can arise through responses uttered silently or aloud. This spectrum — identifying, automatic, memorialized, and uttered — is more nuanced and more precise than the traditional testimonial/physical dichotomy, and gives descriptive power to the rationale underpinning the privilege against self-incrimination. Neurological evidence, like more traditional evidence, may be located on this spectrum, and thus doctrinal riddles of self-incrimination, both modern and ancient, may be solved.
In this paper, we investigate the use of brain activity for person authentication. It has been shown in previous studies that the brain-wave pattern of every individual is unique and that the electroencephalogram (EEG) can be used for biometric identification. EEG-based biometry is an emerging research topic and we believe that it may open new research directions and applications in the future. However, very little work has been done in this area and was focusing mainly on person identification but not on person authentication. Person authentication aims to accept or to reject a person claiming an identity, i.e., comparing a biometric data to one template, while the goal of person identification is to match the biometric data against all the records in a database. We propose the use of a statistical framework based on Gaussian Mixture Models and Maximum A Posteriori model adaptation, successfully applied to speaker and face authentication, which can deal with only one training session. We perform intensive experimental simulations using several strict train/test protocols to show the potential of our method. We also show that there are some mental tasks that are more appropriate for person authentication than others.
Human brain activity collected in the form of Electroencephalography (EEG),
even with low number of sensors, is an extremely rich signal. Traces collected
from multiple channels and with high sampling rates capture many important
aspects of participants' brain activity and can be used as a unique personal
identifier. The motivation for sharing EEG signals is significant, as a mean to
understand the relation between brain activity and well-being, or for
communication with medical services. As the equipment for such data collection
becomes more available and widely used, the opportunities for using the data
are growing; at the same time however inherent privacy risks are mounting. The
same raw EEG signal can be used for example to diagnose mental diseases, find
traces of epilepsy, and decode personality traits. The current practice of the
informed consent of the participants for the use of the data either prevents
reuse of the raw signal or does not truly respect participants' right to
privacy by reusing the same raw data for purposes much different than
originally consented to. Here we propose an integration of a personal
neuroinformatics system, Smartphone Brain Scanner, with a general privacy
framework openPDS. We show how raw high-dimensionality data can be collected on
a mobile device, uploaded to a server, and subsequently operated on and
accessed by applications or researchers, without disclosing the raw signal.
Those extracted features of the raw signal, called answers, are of
significantly lower-dimensionality, and provide the full utility of the data in
given context, without the risk of disclosing sensitive raw signal. Such
architecture significantly mitigates a very serious privacy risk related to raw
EEG recordings floating around and being used and reused for various purposes.
Private companies, government entities, and institutions such as hospitals routinely gather vast amounts of digitized personal information about the individuals who are their customers, clients, or patients. Much of this information is private or sensitive, and a key technological challenge for the future is how to design systems and processing techniques for drawing inferences from this large-scale data while maintaining the privacy and security of the data and individual identities. Individuals are often willing to share data, especially for purposes such as public health, but they expect that their identity or the fact of their participation will not be disclosed. In recent years, there have been a number of privacy models and privacy-preserving data analysis algorithms to answer these challenges. In this article, we will describe the progress made on differentially private machine learning and signal processing.
We initiate the formal study of functional encryption by giving precise definitions of the concept and its security. Roughly
speaking, functional encryption supports restricted secret keys that enable a key holder to learn a specific function of encrypted
data, but learn nothing else about the data. For example, given an encrypted program the secret key may enable the key holder
to learn the output of the program on a specific input without learning anything else about the program.
We show that defining security for functional encryption is non-trivial. First, we show that a natural game-based definition
is inadequate for some functionalities. We then present a natural simulation-based definition and show that it (provably)
cannot be satisfied in the standard model, but can be satisfied in the random oracle model. We show how to map many existing
concepts to our formalization of functional encryption and conclude with several interesting open problems in this young area.
Over the past five years a new approach to privacy-preserving data analysis has born fruit [13, 18, 7, 19, 5, 37, 35, 8, 32]. This approach differs from much (but not all!) of the related literature in the statistics, databases, theory, and cryptography communities, in that a formal and ad omnia privacy guarantee is defined, and the data analysis techniques presented are rigorously proved to satisfy the guarantee. The key privacy guarantee that has emerged is differential privacy. Roughly speaking, this ensures that (almost, and quantifiably) no risk is incurred by joining a statistical database.
In this survey, we recall the definition of differential privacy and two basic techniques for achieving it. We then show some interesting applications of these techniques, presenting algorithms for three specific tasks and three general results on differentially private learning.
We initiate the formal study of functional encryption by giving precise definitions of the concept and its security. Roughly speaking, functional encryption supports restricted secret keys that enable a key holder to learn a specific function of encrypted data, but learn nothing else about the data. For example, given an encrypted program the secret key may enable the key holder to learn the output of the program on a specific input without learning anything else about the program. We show that defining security for functional encryption is non-trivial. First, we show that a natural game-based definition is inadequate for some functionalities. We then present a natural simulation-based definition and show that it (provably) cannot be satisfied in the standard model, but can be satisfied in the random oracle model. We show how to map many existing concepts to our formalization of functional encryption and conclude with several interesting open problems in this young area.
Many people derive peace of mind and purpose in life from their belief in God. For others, however, religion provides unsatisfying answers. Are there brain differences between believers and nonbelievers? Here we show that religious conviction is marked by reduced reactivity in the anterior cingulate cortex (ACC), a cortical system that is involved in the experience of anxiety and is important for self-regulation. In two studies, we recorded electroencephalographic neural reactivity in the ACC as participants completed a Stroop task. Results showed that stronger religious zeal and greater belief in God were associated with less firing of the ACC in response to error and with commission of fewer errors. These correlations remained strong even after we controlled for personality and cognitive ability. These results suggest that religious conviction provides a framework for understanding and acting within one's environment, thereby acting as a buffer against anxiety and minimizing the experience of error.
P300-based Guilty Knowledge Test (GKT) has been suggested as an alternative approach for conventional polygraphy. The purpose of this study was to extend a previously introduced pattern recognition method for the ERP assessment in this application. This extension was done by the further extending the feature set and also the employing a method for the selection of optimal features. For the evaluation of the method, several subjects went through the designed GKT paradigm and their respective brain signals were recorded. Next, a P300 detection approach based on some features and a statistical classifier was implemented. The optimal feature set was selected using a genetic algorithm from a primary feature set including some morphological, frequency and wavelet features and was used for the classification of the data. The rates of correct detection in guilty and innocent subjects were 86%, which was better than other previously used methods.
The basic rationale of P300-based tests of concealed information compares responses to critical ('probe') and non-critical ('irrelevant') items. Accuracy, both in the laboratory and the field, is the degree to which responding to probes exceeds that to irrelevants. The present laboratory study assessed the influence of two factors on accuracy. The first, varied between subjects, was whether the paradigm included probes, irrelevants, and target items (as is the case in most P300 preparations), or whether the paradigm included only probe and irrelevant items. The second, orthogonally varied, within-subject factor was whether the probe was an autobiographical item (the subject's name), or incidentally acquired (as in common field applications). Accuracy was greater with the subject's name as probe, perhaps because of the greater potency of autobiographical items than incidentally acquired ones, even when these are learned to a 100% accuracy. On the other hand, contrary to expectations from a work-load interpretation, the removal of the target did not affect accuracy, but rather decreased P300 magnitude to both probes and irrelevants in the non-target group.
Kokoon eeg headphones
- Kokoon Eeg Headphones
How susceptible is the brain to the side-channel private information extraction
- T L B M T Bonaci
- J Herron
- H J Chizeck
Guessing what's on your mind: Using the n400 in brain computer interfaces
- C Mühl
- B Reuderink
- M Poel
- Y Yao
- R Sun
- T Poggio
- J Liu
- N Zhong
- J Huang
A fully homomorphic encryption scheme [ph. d. thesis]
- C Gentry
App stores for the brain: Privacy & security in brain-computer interfaces
- T Bonaci
- R Calo
- H J Chizeck
Are your thoughts your own
- N Y C B Association
Brain-computer interface anonymizer
- H J Chizeck
- T Bonaci